APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. Results: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. Availability and Implementation: APNet's R, Python scripts and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet研究背景：血浆蛋白质组学的计算分析可通过揭示与不良临床结局相关的分子、细胞表型及信号通路模式，为COVID-19等复杂疾病提供转化研究视角。当前的计算（in silico）方法虽整合了差异表达分析、生物统计学与机器学习手段，但往往忽略了蛋白质组的非线性动态变化（如翻译后修饰），且除特征排序外，其生物学可解释性较为有限。 研究结果：本研究提出APNet——一种全新的计算流程，将基于SJARACNe共表达网络的差异活性分析，与PASNet（一种融合生物学先验知识的稀疏深度学习模型）相结合，用于对COVID-19病情严重程度开展可解释性预测。APNet的驱动因子-通路网络会整合共表达与分类权重，以辅助结果解读与假说构建。在三个COVID-19蛋白质组数据集的患者分类任务中，APNet的表现优于其他模型；其不仅识别出了具有预测价值的驱动因子与通路（其中部分已被单细胞组学研究证实），还揭示了COVID-19中尚未被充分探索的生物标志物调控环路。 数据与代码获取：APNet的R语言、Python脚本及Cytoscape分析方法可通过以下链接获取：https://github.com/BiodataAnalysisGroup/APNet